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# Appendix Figure 2 1a
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#
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# Content
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# 1) Dependencies
# 2) Load Data
# 3) Aggregation for Figure
# 4) Figure
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# 1) Dependencies
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library(readr)
library(dplyr)
library(plyr)
library(ggplot2)
library(gganimate)
library(ggeffects)
library(ggExtra)
library(ggridges)
library(ggrepel)
library(grid)
library(scales)
library(lubridate)
library(extrafont)
library(reshape2)
library(here)
library(ggforce)
library(png)
library(readxl)
library(grid)
library(gridExtra)
library(ggpubr)
library(ggalt)
library(stringr)
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# 2) Load Data
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# Set Path
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
rm(list=ls())

# Custom functions
# ggplot rescale x axis....
scale_x_reordered <- function(..., sep = "___") {
  reg <- paste0(sep, ".+$")
  ggplot2::scale_x_discrete(labels = function(x) gsub(reg, "", x), ...)
}
# ggplot order over facets...
reorder_within <- function(x, by, within, fun = mean, sep = "___", ...) {
  new_x <- paste(x, within, sep = sep)
  stats::reorder(new_x, by, FUN = fun)
}

suppressWarnings(source('ggplot_theme_ddl.R', encoding = "UTF-8"))

# Load Data
df <- readRDS("../data/smd_ner_2015_2019_combined.RDS")
candidates_list_15 <- read.csv('../support/candidates-2015/00-Named_Entity_List_withID.csv', stringsAsFactors = F) %>% 
  as_tibble %>% mutate(id=as.character(id))
candidates_list_19 <- read.csv('../support/candidates-2019/00-Named_Entity_List_withID.csv', stringsAsFactors = F) %>% 
  as_tibble %>% mutate(id=as.character(id))

candidates_list_19 <- candidates_list_19 %>% mutate(candidacy = as.character(gsub("\\s", " ", council))) %>% 
  mutate(council = case_when(candidacy %in% c("SR", "Former Staenderat", "Former Staenderat") ~ "sr",
                             candidacy %in% c("NR", "Former Nationalrat", "Former Nationalrat") ~  "nr",
                             candidacy %in% c("SR und NR", "NR und SR") ~ "sr & nr")) %>% 
  dplyr::select(-c(candidacy))

df$year <- as.character(df$year)
df$date <- format(as.Date(df$date, "%m-%d"), format = "%m-%d")
df$fullname <- ifelse(df$fullname == "Adèle Goumaz", "Adèle Thorens Goumaz", df$fullname)
df$fullname <- ifelse(df$fullname == "Niklaus-Samuel Gugger", "Nik Gugger", df$fullname)
df$incumbent <- ifelse(df$fullname == "Philipp Müller", 1, df$incumbent)

# Remove Federal Councilors
council <- T
# Remove Party Presidents
president <- T

# Council members 2015
council_15 <- c("Ueli Maurer", "Alain Berset", "Didier Burkhalter",
                "Simonetta Sommaruga", "Eveline Widmer Schlumpf",
                "Johann Schneider-Ammann", "Doris Leuthard")

# Council members 2019
council_19 <- c("Ueli Maurer", "Alain Berset", "Ignazio Cassis",
                "Simonetta Sommaruga", "Guy Parmelin",
                "Karin Keller-Sutter", "Viola Amherd")

# Party Presidents 2015
presi_15 <- c("Toni Brunner", "Christian Levrat", "Philipp Müller",
              "Christophe Darbellay", "Regula Rytz", "Martin Bäumle", "Martin Landolt")

# Party Presidents 2019
presi_19 <- c("Albert Rösti", "Christian Levrat", "Petra Gössi",
              "Gerhard Pfister", "Regula Rytz", "Jürg Grossen", "Martin Landolt")


# Remove Council Members:
if(council == T){
  df <- df %>% dplyr::filter((year == "2015" & !fullname %in% council_15) |
                               (year == "2019" & !fullname %in% council_19))
}
# Remove Party Presidents:
if(president == T){
  df <- df %>% dplyr::filter((year == "2015" & !fullname %in% presi_15) |
                               (year == "2019" & !fullname %in% presi_19))
}
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# 3) Aggregations for Figure
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names(df)

agg <- df %>% filter(gender %in% c("f", "m")) %>%
  group_by(year, fullname, gender, party) %>% 
  dplyr::summarise(n = n()) %>% 
  ungroup %>% group_by(year, gender) %>%
  slice_max(n = 10, order_by = n)

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# 4) Figure
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# party orders, colors
.fill2 <- unlist(colourList[['colour']][['parties']])
names(.fill2) <- toupper(names(.fill2))

.fill2 <- .fill2[unique(agg$party)]

f5 <- ggplot(agg, aes(x=reorder_within(fullname, n, year), y=n, color = party, fill = party)) +
  geom_bar(stat = "identity") +
  facet_wrap(gender~year, scales = "free", 
             labeller= labeller(gender = c(`f` = "Women", `m` = "Men"),
                                year = c(`2015` = "2015", `2019` = "2019"))) + 
  scale_x_reordered(expand = c(0,0)) + 
  scale_color_manual(values=.fill2) +
  scale_fill_manual(values=.fill2) +
  coord_flip() +
  labs(title = "Total Mentions of Top 10 Candidates by Gender and Year",
       y = "Total number of Mentions", color = "Party:", fill = "Party:") +
  ddl_theme(type = 'default',
            panel.grid.major=element_blank(),
            legend.position='none',
            axis.line.y.left = element_line(colour="black"),
            axis.line.x.bottom = element_line(colour="black")) +
  theme(legend.position = "bottom", legend.direction = "horizontal",
        axis.title.y = element_blank(),
        axis.title.x = element_text(size = 16),
        plot.title = element_text(size = 20),
        legend.text = element_text(size = 16),
        axis.text.x = element_text(angle = 0, hjust = .5, vjust = 1, size = 16),  
        axis.text.y = element_text(hjust=.5, size = 16),
        strip.text.x = element_text(size = 16),
        plot.margin = unit(c(.5,.5,.5,.6), "cm"))

f5

ggsave(plot = f5, filename='../img_appendix/afigure_2_1a.png', width=12, height=8) 

